code: def test_ppo_head():
      agent_parameters = ClippedPPOAgentParameters()
      num_actions = 5
      action_space = DiscreteActionSpace(num_actions=num_actions)
      spaces = SpacesDefinition(
          state=None, goal=None, action=action_space, reward=None
      )
      head = PPOHead(
          agent_parameters=agent_parameters,
          spaces=spaces,
          network_name="test_ppo_head",
      )
  
      head.initialize()
  
      batch_size = 15
      middleware_data = mx.nd.random.uniform(shape=(batch_size, 100))
      probs = head(middleware_data)
      assert probs.ndim == 2  # (batch_size, num_actions)
      assert probs.shape[0] == batch_size
      assert probs.shape[1] == num_actions
  
imports: import mxnet
  import numpy
  import os
  import pytest
  from scipy import stats
  import sys
  from rl_coach.architectures.head_parameters import PPOHeadParameters
  from rl_coach.architectures.mxnet_components.heads.ppo_head import CategoricalDist, MultivariateNormalDist, DiscretePPOHead, ClippedPPOLossDiscrete, ClippedPPOLossContinuous, PPOHead
  from rl_coach.agents.clipped_ppo_agent import ClippedPPOAlgorithmParameters, ClippedPPOAgentParameters
  from rl_coach.spaces import SpacesDefinition, DiscreteActionSpace
label_desc: Use image node_id 19 for calling a global function with example usage: test_ppo_head() without return types